Lots of efforts have been done in crowd counting. We generally classify these methods into detection-based methods and regression-based methods. The detection-based methods [1]}, [2]}, [3]}, [4]} obtain the crowd count by counting the number of positive detections. The regression-based methods [5]}, [6]}, [7]}, [2]} utilize the mapping between extracted features and cropped patches to regress the crowd count. However, the aforementioned methods are complex, because their features need to be extracted by extremely numerous and cumbersome designs.
In recent years, deep learning has obtained breakthroughs in many fields [9]}, [10]}, [11]}. Many CNN-based methods [12]}, [13]}, [14]} have been proposed in crowd counting. They learn the non-linear mapping between training images and crowd counts. Researchers employ these CNN models to generate a density map that records the count and spatial information of the crowd at each pixel location, then the density map is integrated to get the crowd count.
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